EDLD 610 Winter 2020 Lab 4
Jim Wright
2/5/2020
Lab 4
Colors Assigned 2/6/19, Due 2/11/19 Overview The purpose of this lab is to use color to your advantage. You will be asked to use a variety of color palettes, and use color for its three main purposes: (a) distinguish groups from each other, (b) represent data values, and (c) highlight particular data points.
Data
We’ll be working with the honey production data from #tidytuesday. The repo contains the full data, but we’ll work with just the cleaned up version, using the honeyproduction.csv file, which is posted on canvas or can be obtained by downloading the zip file from the repo.
Assignment
- Visualize the total production of honey across years by state. Use color to highlight the west coast (Washington, Oregon, and California).
- Reproduce the plot according three different kinds of color blindness, as well as a desaturated version.
- Reproduce the plot using a color blind safe pallette.
- Download the file here denoting the region and division of each state.
- Join the file with your honey file.
- Produce a bar plot displaying the average honey for each state across years.
- Use color to highlight the region of the country the state is from.
- Note patterns you notice.
- Create a heatmap displaying the average honey production across years by region.
- Create at least one more plot of your choosing using color to distinguish, represent data values, or highlight. If you are interested in producing maps, I would recommend joining the data with the output from ggplot2::map_data(“state”). But be careful with keys and that you don’t end up with a many-to-many join. See here for additional help.
Finishing up
When you have finished the above, upload your rendered (knit) HTML file to canvas.
Promt 1: Visualize the total production of honey across years by state. Use color to highlight the west coast (Washington, Oregon, and California).
## # A tibble: 6 x 8
## state numcol yieldpercol totalprod stocks priceperlb prodvalue year
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 AL 16000 71 1136000 159000 0.72 818000 1998
## 2 AZ 55000 60 3300000 1485000 0.64 2112000 1998
## 3 AR 53000 65 3445000 1688000 0.59 2033000 1998
## 4 CA 450000 83 37350000 12326000 0.62 23157000 1998
## 5 CO 27000 72 1944000 1594000 0.7 1361000 1998
## 6 FL 230000 98 22540000 4508000 0.64 14426000 1998
2. Reproduce the plot according three different kinds of color blindness, as well as a desaturated version.
3. Reproduce the plot using a color blind safe pallette.
4. Download the file here denoting the region and division of each state.
- Join the file with your honey file.
- Produce a bar plot displaying the average honey for each state across years.
- Use color to highlight the region of the country the state is from.
- Note patterns you notice.## # A tibble: 6 x 8
## state numcol yieldpercol totalprod stocks priceperlb prodvalue year
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 AL 16000 71 1136000 159000 0.72 818000 1998
## 2 AZ 55000 60 3300000 1485000 0.64 2112000 1998
## 3 AR 53000 65 3445000 1688000 0.59 2033000 1998
## 4 CA 450000 83 37350000 12326000 0.62 23157000 1998
## 5 CO 27000 72 1944000 1594000 0.7 1361000 1998
## 6 FL 230000 98 22540000 4508000 0.64 14426000 1998## # A tibble: 6 x 4
## state_name state region division
## <chr> <chr> <chr> <chr>
## 1 Alaska AK West Pacific
## 2 Alabama AL South East South Central
## 3 Arkansas AR South West South Central
## 4 Arizona AZ West Mountain
## 5 California CA West Pacific
## 6 Colorado CO West Mountain## # A tibble: 6 x 11
## state numcol yieldpercol totalprod stocks priceperlb prodvalue year
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 AL 16000 71 1136000 1.59e5 0.72 818000 1998
## 2 AZ 55000 60 3300000 1.48e6 0.64 2112000 1998
## 3 AR 53000 65 3445000 1.69e6 0.59 2033000 1998
## 4 CA 450000 83 37350000 1.23e7 0.62 23157000 1998
## 5 CO 27000 72 1944000 1.59e6 0.7 1361000 1998
## 6 FL 230000 98 22540000 4.51e6 0.64 14426000 1998
## # … with 3 more variables: state_name <chr>, region <chr>, division <chr>## # A tibble: 6 x 3
## # Groups: state [6]
## state region mean_honey
## <chr> <chr> <dbl>
## 1 AL South 825467.
## 2 AR South 2810400
## 3 AZ West 2032267.
## 4 CA West 23169000
## 5 CO West 1750600
## 6 FL South 16469867.
## The Dakotas are the states where the majority of honey is produced, and the midwest region is the region with the most honey produced. I question the classification of Montana as a West region state and would argue it belongs in the Midwest, which would further burgeon the production of Midwest honey as a whole.
5. Create a heatmap displaying the average honey production across years by region.
6. Create at least one more plot of your choosing using color to distinguish, represent data values, or highlight. If you are interested in producing maps, I would recommend joining the data with the output from ggplot2::map_data(“state”). But be careful with keys and that you don’t end up with a many-to-many join. See here for additional help.
## # A tibble: 6 x 11
## state numcol yieldpercol totalprod stocks priceperlb prodvalue year
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 AL 16000 71 1136000 1.59e5 0.72 818000 1998
## 2 AZ 55000 60 3300000 1.48e6 0.64 2112000 1998
## 3 AR 53000 65 3445000 1.69e6 0.59 2033000 1998
## 4 CA 450000 83 37350000 1.23e7 0.62 23157000 1998
## 5 CO 27000 72 1944000 1.59e6 0.7 1361000 1998
## 6 FL 230000 98 22540000 4.51e6 0.64 14426000 1998
## # … with 3 more variables: state_name <chr>, region <chr>, division <chr>
## long lat group order state_name subregion
## 1 -87.46201 30.38968 1 1 Alabama <NA>
## 2 -87.48493 30.37249 1 2 Alabama <NA>
## 3 -87.52503 30.37249 1 3 Alabama <NA>
## 4 -87.53076 30.33239 1 4 Alabama <NA>
## 5 -87.57087 30.32665 1 5 Alabama <NA>
## 6 -87.58806 30.32665 1 6 Alabama <NA>
## # A tibble: 6 x 11
## state numcol yieldpercol totalprod stocks priceperlb prodvalue year
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 AL 16000 71 1136000 1.59e5 0.72 818000 1998
## 2 AZ 55000 60 3300000 1.48e6 0.64 2112000 1998
## 3 AR 53000 65 3445000 1.69e6 0.59 2033000 1998
## 4 CA 450000 83 37350000 1.23e7 0.62 23157000 1998
## 5 CO 27000 72 1944000 1.59e6 0.7 1361000 1998
## 6 FL 230000 98 22540000 4.51e6 0.64 14426000 1998
## # … with 3 more variables: state_name <chr>, region <chr>, division <chr>## # A tibble: 6 x 16
## state numcol yieldpercol totalprod stocks priceperlb prodvalue year
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 AL 16000 71 1136000 159000 0.72 818000 1998
## 2 AL 16000 71 1136000 159000 0.72 818000 1998
## 3 AL 16000 71 1136000 159000 0.72 818000 1998
## 4 AL 16000 71 1136000 159000 0.72 818000 1998
## 5 AL 16000 71 1136000 159000 0.72 818000 1998
## 6 AL 16000 71 1136000 159000 0.72 818000 1998
## # … with 8 more variables: state_name <chr>, region <chr>, division <chr>,
## # long <dbl>, lat <dbl>, group <dbl>, order <int>, subregion <chr>
